Abstract
The vision of the semantic web is to give a semantic perspective to the data so that data become a real-world entity rather than a string of characters. The most important step for achieving this vision is defining and describing the relations among the available data on the web. This is the place where ontologies serve as a backbone for the semantic web. An ontology is a knowledge representation scheme that offers enriched semantic meaning of data. Various ontologies are available on the web within the same or different domains with some common information among them that create a hinder during the map** of the data due to their heterogeneous nature. The ontology alignment is a core solution to resolve this issue; hence, it is demanded to provide a sophisticated ontology alignment approach for semantic map**. This paper defines a universal and Smart Ontology Alignment (SOA) approach for finding relations between entities by dividing the set of attributes of an entity into ‘distinctive features’ and ‘cancellable features’. The SOA approach is termed smart because of the smart knowledge representation scheme it is based upon. State-of-the-art ontology alignment tools do not use this effect and offer wrong relations between the entities. The proposed structure of knowledge is believed to be more natural and comprehensible, and the relations found using SOA increase the performance of the system. The proposed SOA approach is tested with respect to five benchmarks, and the results show that the performance of our approach is near to optimal.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07308-0/MediaObjects/13369_2022_7308_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07308-0/MediaObjects/13369_2022_7308_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07308-0/MediaObjects/13369_2022_7308_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07308-0/MediaObjects/13369_2022_7308_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07308-0/MediaObjects/13369_2022_7308_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07308-0/MediaObjects/13369_2022_7308_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13369-022-07308-0/MediaObjects/13369_2022_7308_Fig7_HTML.png)
Similar content being viewed by others
References
Ngo, D.; Bellahsene, Z.: Overview of YAM++—(not) yet another matcher for ontology alignment task. Web Semant. Sci. Serv. Agents World Wide Web 41, 30–49 (2016)
Ochieng, P.; Kyanda, S.: Large-scale ontology matching: state-of-the-art analysis. ACM Comput. Surv. (CSUR) 51(4), 75 (2018)
Mohammadi, M.; Hofman, W.; Tan, Y.H.: A comparative study of ontology matching systems via inferential statistics. IEEE Trans. Knowl. Data Eng. 31(4), 615–628 (2018)
Patel, A.; Debnath, N.C.; Mishra, A.K.; Jain, S.: Covid19-IBO: a Covid-19 impact on Indian banking ontology along with an efficient schema matching approach. N. Gener. Comput. 39(3), 647–676 (2021)
Rahm, E.: Towards large-scale schema and ontology matching. In Schema matching and map** (pp. 3–27). Springer, Berlin, Heidelberg. (2011)
Verhoosel, J.P.; Van Bekkum, M.; Van Evert, F.: Ontology matching for big data applications in the smart dairy farming domain. In OM (pp. 55–59). (2015)
Salahi, A. and Ansarinia, M.: Predicting network attacks using ontology-driven inference. ar**v preprint ar**v:1304.0913. (2013)
Patel, A.; Sharma, A.; Jain, S.: An intelligent resource manager over terrorism knowledge base. Recent Adv. Comput. Sci. Commun. (Former. Recent Pat. Comput. Sci.) 13(3), 394–405 (2020)
Ehrig, M. and Euzenat, J.: Relaxed precision and recall for ontology matching. In Proc. K-Cap 2005 workshop on Integrating ontology (pp. 25–32). No commercial editor. (2005)
Euzenat, J.: Semantic Precision and Recall for Ontology Alignment Evaluation. In IJCAI (Vol. 7, pp. 348–353). (2007)
Taye, M. and Alalwan, N.: Ontology alignment technique for improving semantic integration. In Proceedings in the Fourth International Conference on Advances in Semantic Processing, Florence, Italy (pp. 13–18). (2010)
Shvaiko, P.; Giunchiglia, F.; Yatskevich, M.: Semantic matching with s-match. In Semantic Web Information Management (pp. 183–202).Springer, Berlin, Heidelberg. (2010)
Giunchiglia, F.; Autayeu, A.; Pane, J.: S-Match: an open source framework for matching lightweight ontologies. Semantic Web 3(3), 307–317 (2012)
Giunchiglia, F.; Shvaiko, P.; Yatskevich, M.: S-Match: an algorithm and an implementation of semantic matching. In European Semantic Web Symposium (pp. 61–75).Springer, Berlin, Heidelberg. (2004)
Faria, D., Martins, C., Nanavaty, A., Oliveira, D., Sowkarthiga, B., Taheri, A., Pesquita, C., Couto, F.M., Cruz, I.F.: AML results for OAEI 2015. In OM. (pp. 116–123). 2015
Ngo, D.; Bellahsene, Z.; Coletta, R.: Yam++-a combination of graph matching and machine learning approach to ontology alignment task. J. Web Semant. 16, 16 (2012)
Gulić, M.; Vrdoljak, B.; Banek, M.: Cromatcher: an ontology matching system based on automated weighted aggregation and iterative final alignment. Web Semant. Sci. Serv. Agents World Wide Web 41, 50–71 (2016)
OAEI: http://oaei.ontologymatching.org/2018/results/anatomy/index.html
Patel, A.; Jain, S.: A novel approach to discover ontology alignment. Recent Adv. Comput. Sci. Commun. (Former. Recent Pat. Comput. Sci.) 14(1), 273–281 (2021)
Jain, S. and Patel, A.: Smart Ontology-Based Event Identification. In 2019 IEEE 13th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) (pp. 135–142). IEEE. (2019)
Jain, S. and Patel, A.: Situation-Aware Decision-Support during Man-Made Emergencies. International Conference on Emerging Trends in Information Technology. Lecture Notes in Electrical Engineering. (2019)
Katis, E.; Kondylakis, H.; Agathangelos, G.; Vassilakis, K.: Develo** an ontology for curriculum and syllabus. In European Semantic Web Conference (pp. 55–59). Springer, Cham. (2018)
Chujai, P.; Kerdprasop, N.; Kerdprasop, K.: On transforming the ER model to ontology using protégé OWL tool. Int. J. Comput. Theory Eng. 6(6), 484 (2014)
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Patel, A., Debnath, N.C. & Jain, S. Facilitating Smart Ontology Alignment Over Comprehensive Knowledge Structure. Arab J Sci Eng 48, 9713–9725 (2023). https://doi.org/10.1007/s13369-022-07308-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-022-07308-0